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RWTH Aachen University
Multidimensional Patterns of Disturbance in Digital Social Networks
Dimitar Denev
Lehrstuhl für Informatik V Information Systems
Prof. Dr. Matthias Jarke
Lehr- und Forschungsgebiet
Knowledge-based Systems
Prof. Gerhard Lakemeyer Ph.D.
Advisors:Ralf KlammaMarc Spaniol
Master Thesis Final Presentation
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Agenda
Motivation
Problem Analysis Approach
State of the Art
Model of Digital Social Networks
Pattern Language
PALADIN
Conclusions and Outlook
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Motivation
Trolls – persons who post only in threads, started by themselves
Context Yahoo! Mailing list „Greek Mythology Link“ Discussion about the movie „Troy“
Message of a troll Troy is a MOVIE – message containing deliberate error Movies are current mythology – message posted as a
generally accepted fact without a proof or analysis Is Christianity and all that other stuff myth, history,
religion or what – inflammatory message including a contemptuous comment on religious thematic.
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Problem Statement
Disturbance as a new source of information and a starting point for learning processes
Hinders the communication in the network Compels individuals to leave the network Difficulties for the disturbances to be discovered or
predicted Multidimensional context of the digital social networks Large size of the networks Knowledge about the disturbances is mostly from
experience and observation
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A pattern language overcomes the difficulties for discovering and describing disturbances
Pattern – a general repeatable solution to a commonly
recurring problem [Alexander 1978]
Machine-readable description of the patterns - XML-
based Pattern Language for Multidimensional
Disturbances
Automatic Analysis of digital social networks for
disturbances with the pattern language
Solution Approach
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Solution Approach
The model of the digital social networks is a based on Actor-Network Theory (ANT) Graph Representation Social Network Analysis (SNA) I* Framework
Multidimensionality of the digital social networks reflected in the model Sociology Computer Science Media Theory Graph Theory Social Capital Theory
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State of the Art
Relations built on the information from Google, Friend-Of-A-Friend network, Bibliography
Dependencies derived from the technical dependencies
Posting in the same thread
Relations
Social Network Analysis, Semantic Web
IndividualsFriend-Of-A-Friend network, Google results
Flink
[Mika 2005]
Temporal Analysis
Developers, Software Components
Eclipse IDE, CVS Repository
Ariadne
[de Souza et al. 2004]
Social Network Analysis, Statistics
Individuals, Mails, Threads, Genres
Mailing ListCOMB [Boudourides et al. 2002]
Analysis Approach
ActorsMedia
Digital Social Networks Projects
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Actor - the basic unit of the model, no difference between technical and social actors.
Semantics, given to the actors from the interpretation in the context of digital social networks: Member – any person or group, part of the digital social
network Medium – an actor which enables the members to
exchange information Artefact – objects created by the members using some
medium Relation – a relation between two actors Network – set of actors along with their relations
Model of Digital Social NetworksActor-Network Theory [Latour 1997]
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Digital Social Network
Digital Media
I* Dependencies
Members
Artefacts
Member Network
Model of Digital Social Networks
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Member types defined according to patterns of
behavior Answering Person Questioner Troll Spammer Conversationalist
Member properties, defined with the help of SNA Centrality types: degree centrality, closeness centrality,
betweenness centrality - determined by the position of the member in the network
Efficiency – describes the existence of structural holes
Model of Digital Social NetworksMembers
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Medium – an actor which enables the members to exchange information Every network supports a set of media A medium affords the creation of a certain set of
artefacts Media types
Email Discussion group Chat room Blog
Wiki
Transaction-based web sites
URL
Model of Digital Social NetworksMedia
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Artefact – objects created by the members using some medium
Artefact types Message Burst Thread Blog entry Comment
Conversation
Feedback (Rating) Artefact properties – author, date of creation, reply to
Model of Digital Social NetworksArtefacts
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I* Dependency types Goal Resource Task Soft goal
Dependencies in digital social networks Structural dependencies
Communication dependency Cross-media dependencies
Coordination dependency Artefact dependency
Model of Digital Social NetworksI* Framework [Yu et al. 1997]
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Network
Coordinator
Gatekeeper
Hub
Member
Iterant Broker
URL
isA
isA
isA
isA
Coordination
Artefact
Communication
Model of Digital Social NetworksI* Dependencies Example
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State of the ArtPattern Languages Projects
„Asynchronous collaborative learning“, „Student group management“
no patterns available
„Working in small groups“, „Overlapping responsibilities“
„Citizen access to simulations“, „Online Community Service Engine“
Pattern Examples
XML SchemaSynopsis, Problem, Context, Forces, Rationale, Pattern Link
Human-Computer Interface
PLML
[Fincher 2004]
Not available
Not available
Not available
Formal Definition
Problem, Analysis, Solution, Context
e-LearningE-LEN
[Steeples et al. 2004]
Essence, Context, Discussion, Implication, Pattern Relations
Computer-Supported Collaborative Work
PoInter
[Viller et al. 2000]
Problem, Context, Discussion, Solution
Social Studies
Public Sphere Project [Schuler 2002]
Pattern StructureDomain
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Pattern – a general repeatable solution to a commonly
recurring problem [Alexander 1978]
Pattern structure
Disturbance
Forces and force relations
Solution
Rationale
Dependencies
Pattern relations
Pattern LanguagePattern Structure
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Variables – simple variables (troll, thread), properties (thread.author) and set variables (v1,…,vn).
Operations Arithmetic (+, -, *, / ) Aggregate (SUM, COUNT, AVERAGE) Logical (&, |, ~, FORALL and EXISTS) Comparison (=, !=, >, <).
Rules for variable binding Simple variables – pattern parameters, actors or set
variables Properties – actor properties or relations Set variables – actors
Interpreted by a finite state automaton
Pattern LanguageFELP
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Troll Pattern: This pattern tries to discover the cases when a troll exists in a digital social network. A troll in the network is considered a disturbance.
Disturbance: (EXISTS [medium | medium.affordance = threadArtefact]) &
(EXISTS [troll |(EXISTS [thread | (thread.author = troll) &
(COUNT [message | (message.author = troll) &
(message.posted = thread)]) > minPosts]) &
(~EXISTS[ thread1, message1| (thread1.author1 != troll) &
(message1.author = troll & message1.posted = thread1 ]))])])
Forces: medium; troll; network; member; thread; message; url
Force Relations: neighbour(troll, member); own thread(troll, thread)
Solution: No attention must be paid to the discussions started by the troll.
Rationale: The troll needs attention to continue its activities. If no attention is paid, he/she will stop participating in the discussions.
Pattern Relations: Associates Spammer pattern.
Pattern LanguageSample Pattern
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Pattern
Disturbance
Variables
Pattern Template
Disturbance
VariablesPattern Parameters
v1,...,vn – variables bound to actors a1,...,an
p1,…, pm – pattern parameters
d – disturbance with d=(v1,...,vn, p1,…, pm).
μ1,…, μm – substitutions for the pattern parameters
Set Pattern Parameters: d = d(v1,...,vn, p1/μ1,…, pm/μm)
Pattern LanguageAlgorithm for Pattern Application
1. Set pattern parameters
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Pattern
Disturbance
Variables
Pattern Template
Disturbance
VariablesPattern Parameters
Pattern Template Instance
Disturbance Instances
Variables Pattern Parameters
Digital Social Network
α1,..., αk – actor instances in the social network
I(ai)=(αi1,…,αir) – instances of
the actor ai
S = (s1,…,st)= I(a1)×…×I(an)
Pattern LanguageAlgorithm for Pattern Application
1. Set pattern parameters
2. Instantiate disturbances
Instantiate disturbances: D = (d(s1),…, d(sp)),
where d(si) = d(v1/α i1,...,vn/αin,p1/μ1,…,pm/μm)
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Pattern
Disturbance
Variables
Pattern Template
Disturbance
VariablesPattern Parameters
Pattern Template Instance
Pattern Instance
Disturbance
Variables Pattern Parameters
Forces ForceRelations
Rationale
Dependencies
Description Solution
Pattern Relations
Disturbance Instances
Variables Pattern Parameters
Digital Social Network
Pattern LanguageAlgorithm for Pattern Application
1. Set pattern parameters
2. Instantiate disturbances
3. Evaluate disturbances
4a. Change Pattern Parameters
4b. Apply Pattern Solution
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ANT Subsystem Web Interface XML Repository
Pattern Subsystem Formal Expression Module XML Pattern Repository Web Interface
Social Network Subsystem Base Social Network Module JUNG Interface IBM DB2 Database
Pattern Application Module Formal Expression Evaluation Pattern Instance Repository
PALADINArchitecture Implementation
PALADIN – PAttern LAnguage for DIsturbances in digital social Networks
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Step 1: define disturbance
expression enter pattern properties
Step 2: bind variables to actors store pattern in the
pattern repository
PALADINWeb Interface
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Troll
Spammers
Members
Size reflects centrality of
the member
Members who participate
in other disturbances,
such as bursts or
structural holes can be
displayed as well
PALADINJUNG Interface Extension
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Case study - 10 patterns of disturbance over 119 social network instances, 17359 individuals, 215 345 mails
PALADINResults
Occurs in big networks where the members are distributed in different clusters.
40No Leader
Occurs for members having neighbours with only one contact.67Structural Hole
Occurs in large networks where disconnected subnetworks exist. Scalability is necessary.
13Independent Discussions
The pattern occurs in the network centered around a member.37Leader
Spammers can be found often in discussion groups. False positives exist.
86Spammer
Troll occurs very rarerly in cultural communities. True negatives exist.
2Troll
Occurs in small networks. The effects of the lack of an answering person must be further checked with content analysis.
61No Answering Person
The existence implies that the network is not popular.67No Questioner
The existence implies little communication in the network.76No Conversationalist
The pattern finds out topics which were very important for certain period of time. Scalability is necessary.
22Burst
RemarksOccurrencesPattern
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Depends on the used media in the network
Relations built on the information from Google, FOAF, Mails, Bibliography
Dependencies derived from the technical dependencies.
Posting in the same thread.
Relations
Social Network Analysis, Semantic Web
IndividualsFriend-Of-A-Friend network, Google results
Flink
[Mika 2005]
Disturbance-oriented, Pattern Repository, Social Network Analysis, Temporal Analysis, Statistics
Media, Members, Artefacts
Any Type of Digital Social Network
PALADIN
Temporal AnalysisDevelopers, Software Components
Eclipse IDE, CVS Repository
Ariadne
[de Souza et al. 2004]
Social Network Analysis, Statistics
Individuals, Mails, Threads, Genres
Mailing ListCOMB
[Boudourides et al. 2002]
Analysis ApproachActorsMedia
Conclusion
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Outlook
Interoperability with applications based on Semantic Web, such as Flink
Methodology for visualization of multidimensional disturbances, must reflect Media Artefacts
SWAP-it [Seeling et al. 2004] InfoSky [Tochterman 2002]
Dependencies
Integration with simulation environment for social networks – can predict disturbances earlier
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THANK YOU FOR YOUR ATTENTION!